Top 10 Events

Setup

First, we need to import all the necessary libraries. Some of them are specifically developed to handle the download and plotting of the data and are hosted at the indicators set-up repository in GitHub

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import warnings
warnings.filterwarnings("ignore")

import os
import os.path as op
import sys
import folium

import numpy as np
import pandas as pd

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.colors import get_df_col
from ind_setup.tables import plot_df_table, get_data_metrics, table_temperature_summary

sys.path.append("../../../functions")
from data_downloaders import GHCN

Define location and variables of interest

country = 'Palau'
vars_interest = ['TMIN', 'TMAX']

Observations from Koror Station

The data used for this analysis comes from the GHCN (Global Historical Climatology Network)-Daily database.
This a database that addresses the critical need for historical daily temperature, precipitation, and snow records over global land areas. GHCN-Daily is a composite of climate records from numerous sources that were merged and then subjected to a suite of quality assurance reviews. The archive includes over 40 meteorological elements including temperature daily maximum/minimum, temperature at observation time, precipitation and more.

https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/doc/GHCND_documentation.pdf

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures" 

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if update_data:
    df_country = GHCN.get_country_code(country)
    print(f'The GHCN code for {country} is {df_country["Code"].values[0]}')

    df_stations = GHCN.download_stations_info()
    df_country_stations = df_stations[df_stations['ID'].str.startswith(df_country.Code.values[0])]
    print(f'There are {df_country_stations.shape[0]} stations in {country}')

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if update_data:
    GHCND_dir = 'https://www.ncei.noaa.gov/data/global-historical-climatology-network-daily/access/'
    id = 'PSW00040309' # Koror Station
    dict_min = GHCN.extract_dict_data_var(GHCND_dir, 'TMIN', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
    dict_max = GHCN.extract_dict_data_var(GHCND_dir, 'TMAX', df_country_stations.loc[df_country_stations['ID'] == id])[0][0]
    st_data = pd.concat([dict_min['data'], (dict_max['data'])], axis=1).dropna()
    st_data['diff'] = st_data['TMAX'] - st_data['TMIN']
    st_data['TMEAN'] = (st_data['TMAX'] + st_data['TMIN'])/2
    st_data.to_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))
else:
    st_data = pd.read_pickle(op.join(path_data, 'GHCN_surface_temperature.pkl'))

df = table_temperature_summary(st_data)
fig = plot_df_table(df.T, figsize = (500, 340),)

Monthly

st_data = st_data.resample('M').mean()

nevents = 10
top_10 = st_data.sort_values(by='TMAX', ascending=False).head(nevents)

dict_plot = [{'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'}]
scatter_dict = [{'data' : top_10, 'var' : 'TMAX', 'ax' : 1, 'label' : 'Top 10 Events'}]

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = scatter_dict, figsize = (25, 12))

Generate table

Table sumarizing different metrics of the data analyzed in the plots above

fig = plot_df_table(np.round(top_10, 2)[['TMAX']])

Annual

st_data = st_data.resample('Y').mean()
top_10 = st_data.sort_values(by='TMAX', ascending=False).head(10)

dict_plot = [{'data' : st_data, 'var' : 'TMAX', 'ax' : 1, 'label' : 'TMAX'}]
scatter_dict = [{'data' : top_10, 'var' : 'TMAX', 'ax' : 1, 'label' : 'Top 10 Events'}]
fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = scatter_dict, figsize = (25, 12));

Generate table

Table sumarizing different metrics of the data analyzed in the plots above

fig = plot_df_table(np.round(top_10, 2)[['TMAX']])